🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks in terms of network usage, shell execution, obfuscation, and credential handling. However, the metadata risk score is elevated due to the package's newness and lack of maintainer history, raising suspicion.

Metadata risk score is high due to lack of maintainer history and anonymous author.
Git repository is inaccessible.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is likely newly created and lacks maintainer history, with an anonymous author. The git repository is also not accessible.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agentdraft.io>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentdraft-mcp

Create a mini-application named 'AIWorkbench' using the 'agentdraft-mcp' package. This application will serve as a bridge between various AI clients (such as Claude Desktop, Cursor, Cline, etc.) and your local AI development environment. The primary goal of AIWorkbench is to streamline the process of integrating these AI clients with your local AI models or services by exposing a scheduling source of truth through an MCP (Multi-Client Protocol) server.

### Features:
1. **MCP Server Integration**: Utilize 'agentdraft-mcp' to set up an MCP server that can communicate with MCP-aware AI clients.
2. **Model Management**: Allow users to manage multiple AI models or services from different providers. Users should be able to add, remove, and update model configurations.
3. **Task Scheduling**: Implement a task scheduler that allows users to schedule tasks involving these AI models. Tasks could include training, inference, or data processing.
4. **User Interface**: Develop a simple but intuitive user interface that allows users to interact with their AI models and tasks. Consider using a web framework like Flask or Django for the UI.
5. **Logging & Monitoring**: Provide logging capabilities to monitor the status of tasks and model interactions. Logs should include timestamps, task IDs, and any relevant error messages.
6. **Security**: Ensure that the application handles authentication securely. Users should be required to log in before they can manage their models or tasks.
7. **Documentation**: Create comprehensive documentation for both users and developers. Include setup instructions, API documentation, and usage examples.

### Steps to Build:
1. **Set Up Environment**: Install necessary packages including 'agentdraft-mcp'. Set up a virtual environment for your project.
2. **Design Database Schema**: Plan out how you will store information about users, models, and tasks. Consider using SQLite or PostgreSQL.
3. **Develop MCP Server**: Use 'agentdraft-mcp' to develop the MCP server component. Ensure it can handle requests from MCP-aware clients.
4. **Build User Interface**: Using a web framework of your choice, create a UI where users can manage their models and tasks.
5. **Implement Task Scheduler**: Develop a scheduler that can take in tasks and distribute them according to the specified model configurations.
6. **Add Logging and Monitoring**: Integrate logging into your application to track the execution of tasks and the interaction with models.
7. **Secure Authentication**: Implement a secure login system to ensure only authorized users can access the application.
8. **Testing**: Thoroughly test all components of the application to ensure reliability and performance.
9. **Deployment**: Prepare the application for deployment. Consider hosting options such as Heroku or AWS.
10. **Document Everything**: Write detailed documentation covering installation, configuration, and usage of the application.